Speckle de-noising via deep learning in digital holographic interferometry

Qiang Fang; Haiting Xia; Qinghe Song; Peigen Li

doi:10.1117/12.2642096

20 December 2022 Speckle de-noising via deep learning in digital holographic interferometry

Qiang Fang, Haiting Xia, Qinghe Song, Peigen Li

Proceedings Volume 12318, Holography, Diffractive Optics, and Applications XII; 123181Z (2022) https://doi.org/10.1117/12.2642096
Event: SPIE/COS Photonics Asia, 2022, Online Only

Conference Poster

Abstract

Speckle de-noising can improve digital holographic interferometric phase measurements but may affect experimental accuracy. A deep learning-based speckle de-noising algorithm is developed referring to the U-Net and the DenseNet architectures using a conditional generative adversarial network established by the generator and the discriminator network. The loss functions that guide generator training consist of a mixture of a static spatial distance norm metric designed by considering the peak signal-to-noise ratio parameter, and a dynamic metric generated from the discriminator that grows with the generator in training. Datasets obtained from speckle simulations 4-f system are shown to provide improved noise feature extraction. Therefore, the proposed method offers better performance than other de-noising algorithms For processing experimental strain data from digital holography.

Citation Download Citation

Qiang Fang, Haiting Xia, Qinghe Song, and Peigen Li "Speckle de-noising via deep learning in digital holographic interferometry", Proc. SPIE 12318, Holography, Diffractive Optics, and Applications XII, 123181Z (20 December 2022); https://doi.org/10.1117/12.2642096

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